---
title: "Dashboard"
output: 
  flexdashboard::flex_dashboard:
    theme: readable
    vertical_layout: fill
    social: [ "menu"]
    source_code: embed
    logo: ~/Desktop/data/CSC.png
---

```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(readxl)
library(plotly)
library(plyr)
library(dplyr)
library(highcharter)
library(tidyverse)
library(DT)
library(rpivotTable)
library(viridis)
library(RColorBrewer)
library(leaflet)
library(tm)
library(SnowballC)
library(wordcloud)

#Import Harvard csv file
Harvard <- read_csv("~/Desktop/Data/Harvard.csv")
#Import Udemy csv file
Udemy <- read_csv("~/Desktop/Data/Udemy.csv")
#Import Cluster excel sheel
Hcluster <- read_excel("~/Desktop/Data/Cluster.xlsx")
#Import EDM excel sheel
EDM <- "~/Desktop/Data/EDM.xlsx" %>%  excel_sheets() %>% set_names() %>%  map(read_excel, path = "~/Desktop/Data/EDM.xlsx") 
```

```{r dataprep, include=FALSE}
#create Udemy dataframe
Udemy <- data.frame(rbind(Udemy))
#rename columns of dataframe
UDEMY <- setNames(cbind(rownames(Udemy), Udemy, row.names = NULL), c("No.", "First Name","Last Name", "Agency", "Groups","Course ID","Course Title","Course Duration","% Marked Completed","Mins Video Consumed","Date Enrolled", "Date Started","First Date Completed","Date Completed", "Date Last Accessed","Course Category","Assigned","Assigned By","User is Deactivated"))
#convert email column into agency acronyms
UDEMY$Agency <- str_extract(str_to_upper(UDEMY$Agency), "(?<=\\@)[[:alpha:]]+(?=\\.)" )

#create Harvard dataframe
Hdf <- data.frame(rbind(Harvard))
#convert email column into agency acronyms
Hdf$Email <- str_extract(str_to_upper(Hdf$Email), "(?<=\\@)[[:alpha:]]+(?=\\.)" )
#rename email column as "Agency"
colnames(Hdf)[4] <- "Agency" 
#convert cluster page into dataframe and rename columns
Cluster <- setNames(cbind(rownames(data.frame(rbind(Hcluster))), data.frame(rbind(Hcluster)), row.names = NULL), 
         c("remove", "Agency", "Cluster")) 
#remove unecessary column
Cluster = subset(Cluster, select = -c(remove)) 
#Combine Harvard dataframe with Cluster dataframe
clusteranalysis <- left_join(Hdf, Cluster, by = "Agency") 
#Combine Udemy dataframe with Cluster dataframe
Uclusteranalysis <- left_join(UDEMY, Cluster, by = "Agency") 
``` 

WOG {data-orientation=rows}
=====================================

Row
-------------------------------------

### Total Unique Harvard Users out of 50,000 Harvard Article Licenses 

```{r Harvard users}
#valuebox of WOG mean accesses
valueBox(length(unique(Hdf$UserName)), icon = "fa-user", color = "#D1F2EB") 
```


### Average Article Views Per User 

```{r Learner1}
#extract columns needed
Hkeep <- c("UserName","Cluster","Format") 
#create new dataframe of selected columns
ldf = clusteranalysis[Hkeep]
tabl <-as.data.frame(table(ldf$UserName))
meanH <- round(mean(tabl$Freq),2)
#valuebox of mean views
valueBox(meanH, icon = "fa-eye", color = "#E8F8F5") 
```

### Percentage Harvard Licence Utilisation

```{r harvard utilisation}
#Gauge widget of harvard licence utilisation percentage
gauge(round(mean((length(unique(Hdf$UserName))/50000)*100),  
            digits = 2),
            min = 0,
            max = 100, 
            symbol = '%',
            label = "Utilisation",
            gaugeSectors(success = c(50, 100),
                         warning = c(16, 49),
                         danger = c(0, 15),
                         colors = c("#1E8449", "#F7DC6F", "#C0392B")))
```



### Total Unique Udemy Users out of 45,000 Udemy Course Licenses 

```{r Udemy users}
#valuebox of unique udemy users
valueBox(length(unique(Udemy$User.Email)), icon = "fa-user", color = "#D4E6F1") #WOG mean accesses
```


### Average User Course Attendance

```{r Learner2}
Uclusteranalysis["User Email"] <- Udemy$User.Email
#extract columns needed
Ukeep <- c("User Email","Cluster")
udf = Uclusteranalysis[Ukeep]
tablu <- as.data.frame(table(udf$`User Email`))
meanU <- round(mean(tablu$Freq),2)
#valuebox of WOG mean accesses
valueBox(meanU, icon = "fa-graduation-cap", color = "#EAF2F8") 
```


### Percentage Udemy Licence Utilisation 

```{r percentage course completion}
#gauge of udemy average course completion rate
gauge(round(mean((length(unique(Udemy$User.Email))/46000)*100),
            digits = 2),
            min = 0,
            max = 100,
            symbol = '%',
            label = "Completion",
            gaugeSectors(success = c(50, 100),
                         warning = c(16, 49),
                         danger = c(0, 15),
                         colors = c("#A3E4D7", "#F7DC6F", "#C0392B")))
```

Column 
----------------------------------- 

### Harvard Subjects

```{r WOG chart Harvard 1}
#Group Harvard dataframe by subject and their respective counts
p1 <- Hdf %>%
         group_by(Subject) %>%
         summarise(count = n())

#Rank in bars ascending order of counts
p1$Subject <- factor(p1$Subject, levels = unique(p1$Subject)[order(p1$count, decreasing = FALSE)]) 

#Plot bar chart of subjects based on popularity 
plot_ly(p1, x = ~count, y = ~Subject, color = ~Subject, colors = "BrBG", alpha = 0.5, type = 'bar') %>% 
    layout(yaxis = list(title = 'Subject'), xaxis = list(title = "Count"), showlegend=FALSE)
```


### Udemy Categories 

```{r WOG chart Harvard 2}
#Shorten titles that have commas
UDEMY$Subject <- gsub(",.*$", "", UDEMY$`Course Category`) 
#Group Udemy dataframe by category and their respective counts
p2 <- UDEMY %>%
         group_by(Subject) %>%
         summarise(count = n())
#Extract only the top 10 categories
p2 <-top_n(p2,10)
#Rank in ascending order
p2$Subject <- factor(p2$Subject, levels = unique(p2$Subject)[order(p2$count, decreasing = FALSE)]) 
#Plot bar chart of categories based on count 
plot_ly(p2, x = ~count, y = ~Subject, color = ~Subject, colors = "BrBG", alpha = 0.5, type = 'bar') %>% layout(yaxis = list(title = 'Top 10 Categories'),xaxis = list(title = "Count"), showlegend=FALSE)
```


Cluster 
===========================================

Column  {.tabset .tabset-fade}
----------------------------------- 

### Month-Harvard

```{r month H}
#keep desired columns 
clusterH = select(clusteranalysis, 5,6,7,8,9,13,14,15,19)
#rename fourth column to "Month"
colnames(clusterH)[4] <- "Month"
clusterH$Month <- as.Date(as.character(clusterH$Month), format="%d-%b-%y")
#display dates as solely year and month
clusterH$Month<- format(as.Date(clusterH$Month), "%Y-%m")
#keep 2 columns only
keep <- c("Month","Cluster")
df1 <- table(clusterH[keep])
dfh <- as.data.frame(df1)

#keep desired columns 
clusterU <- Uclusteranalysis[c(-1,-2,-3,-5,-6,-8,-10,-19,-18,-17,-11,-15,-13,-14) ]
#rename fourth column to "Month"
colnames(clusterU)[4] <- "Month"
#use dates no earlier than 180 days before today
clusterU <- subset(clusterU, clusterU$Month > (Sys.Date() - 180)) 
#display dates as solely year and month
clusterU$Month<- format(as.Date(clusterU$Month), "%Y-%m")
#keep 2 columns only
keep <- c("Month","Cluster")
df2 <- table(clusterU[keep])
dfu <- as.data.frame(df2)
#combine dataframes
dfu <- left_join(dfh, dfu, by = "Month") 
#plot harvard views by month
plot_ly(dfh, x = ~Month, y = ~Freq, color = ~Cluster, colors = "Spectral") %>%
  add_bars() %>% layout(legend = list(x = 0, y = 1), xaxis = list( type = 'date', tickformat = "%b %Y"))
```


### Harvard Totals

```{r Harvard overview}
#group harvard data by cluster and obtain counts for each cluster
totalH <- clusteranalysis %>% group_by(Cluster) %>% tally()
#plot bar chart of total views by cluster
plot_ly(totalH, x = ~Cluster, y = ~n, color = ~Cluster, colors = "Spectral", type = 'bar') %>%
layout(yaxis = list(title = 'Total Harvard Titles'),xaxis = list(title = "Cluster")) %>% layout(showlegend=FALSE)
```


### Views per learner 

```{r Learner 1}
names(tabl) <- c("Agency", "Count")
tabl$Agency <- str_extract(str_to_upper(tabl$Agency), "(?<=\\@)[[:alpha:]]+(?=\\.)" )
tablH <- left_join(tabl, Cluster, by = "Agency")
pal1 <- colorNumeric(
   palette= "Spectral",
   domain= tablH$Count)
tablh <- tablH[order(as.integer(tablH$Count),decreasing = FALSE), ]
plot_ly(tablh, type = "bar", x = ~Cluster, y = ~Count, color = ~pal1(Count)) %>% layout(barmode = "stack") %>% layout(xaxis = list(title="Learner's Cluster"), yaxis = list(title="Cumulative View Count"), showlegend=FALSE)
```


### Top Central

```{r Central Admin Summary H}
#Harvard Central Admin Datatable
CAtitle <- filter(clusteranalysis,Cluster == "Central Administration") %>% group_by(Asset) %>% summarise(Cluster=n())
CAtitle <- CAtitle  %>%
     arrange(desc(Cluster))
CAtitle$row_num <- seq.int(nrow(CAtitle))
CAtitle <- CAtitle[,c(3,1,2)]
datatable(CAtitle, rownames = FALSE, colnames=c('Harvard Titles for Central Administration', 'Count'))

```




### Top Infrastructure

```{r Infrastructure summary H}
#Harvard Infrastructure Datatable
INtitle <- filter(clusteranalysis,Cluster == "Infrastructure and Environment") %>% group_by(Asset) %>% summarise(group=n())
INtitle <- INtitle  %>%
     arrange(desc(group))
INtitle$row_num <- seq.int(nrow(INtitle))
INtitle <- INtitle[,c(3,1,2)]
datatable(INtitle, rownames = FALSE, colnames=c('Harvard Titles for Infrastructure & Environment', 'Count'))
```

### Top Social

```{r Social summary H}
#Harvard Social Datatable
SOtitle <- filter(clusteranalysis,Cluster == "Social") %>% group_by(Asset) %>% summarise(Cluster=n())
SOtitle <- SOtitle  %>%
     arrange(desc(Cluster))
SOtitle$row_num <- seq.int(nrow(SOtitle))
SOtitle <- SOtitle[,c(3,1,2)]
datatable(SOtitle, rownames = FALSE, colnames=c('Harvard Titles for Social Sector', 'Count'))
```


### Top Security

```{r Security summary H}
#Harvard Security Datatable
SEtitle <- filter(clusteranalysis,Cluster == "Security") %>% group_by(Asset) %>% summarise(Cluster=n())
SEtitle <- SEtitle  %>%
     arrange(desc(Cluster))
SEtitle$row_num <- seq.int(nrow(SEtitle))
SEtitle <- SEtitle[,c(3,1,2)]
datatable(SEtitle, rownames = FALSE, colnames=c('Harvard Titles for Security Sector', 'Count'))
```


### Top Economy

```{r Economy summary H}
#Harvard Economy Datatable
ECtitle <- filter(clusteranalysis,Cluster == "Economy Building") %>% group_by(Asset) %>% summarise(Cluster=n())
ECtitle <- ECtitle  %>%
     arrange(desc(Cluster))
ECtitle$row_num <- seq.int(nrow(ECtitle))
ECtitle <- ECtitle[,c(3,1,2)]
datatable(ECtitle, rownames = FALSE, colnames=c('Harvard Titles for Economy Sector', 'Count'))
```



Column  {.tabset .tabset-fade}
----------------------------------- 


### Month-Udemy

```{r month U}
#Barplot of Udemy accesses per month by cluster
dfu <- dfu %>% rename(Freq = Freq.y)
plot_ly(dfu, x = ~Month, y = ~Freq, color = ~Cluster.y, colors = "Spectral") %>%
  add_bars() %>% layout(legend = list(x = 0, y = 1), xaxis = list( type = 'date', tickformat = "%b %Y"))
```

### Udemy Totals

```{r Udemy overview}
totalU <- Uclusteranalysis %>% group_by(Cluster) %>% tally() 
plot_ly(totalU, x = ~Cluster, y = ~n, color = ~Cluster, colors = "Spectral", type = 'bar') %>%
layout(yaxis = list(title = 'Total Udemy Titles'),xaxis = list(title = "Cluster")) %>% layout(showlegend=FALSE)
```


### Attendance per learner 

```{r Learner 2}
tablu <- tablu %>% rename( Count= Freq, Agency = Var1) 
tablu$Agency <- str_extract(str_to_upper(tablu$Agency), "(?<=\\@)[[:alpha:]]+(?=\\.)" )
tablU <- left_join(tablu, Cluster, by = "Agency")
pal2 <- colorNumeric(
   palette= "Spectral",
   domain= tablU$Count)
tablU <- tablU[order(as.integer(tablU$Count),decreasing = FALSE), ]
plot_ly(tablU, type = "bar", x = ~Cluster, y = ~Count, color = ~pal2(Count), alpha=0.7) %>% layout(barmode = "stack") %>% layout(xaxis = list(title="Learner's Cluster"), yaxis = list(title="Cumulative Access Count"), showlegend=FALSE)
```


### Top Central

```{r Central Admin Summary U}
#Udemy
CAcat <- filter(Uclusteranalysis,Cluster == "Central Administration") %>% group_by(`Course Title`) %>% summarise(Cluster=n())
CAcat <- CAcat  %>%
     arrange(desc(Cluster))
CAcat$row_num <- seq.int(nrow(CAcat))
CAcat <- CAcat[,c(3,1,2)]
datatable(CAcat, rownames = FALSE, colnames=c('Udemy Titles for Central Administration', 'Count'))
```


### Top Infrastructure

```{r Infrastructure summary U}
INcat <- filter(Uclusteranalysis,Cluster == "Infrastructure and Environment") %>% group_by(`Course Title`) %>% summarise(Cluster=n())
INcat <- INcat  %>%
     arrange(desc(Cluster))
INcat$row_num <- seq.int(nrow(INcat))
INcat <- INcat[,c(3,1,2)]
datatable(INcat, rownames = FALSE, colnames=c('Udemy Titles for Infrastructure & Environment', 'Count'))
```


### Top Social

```{r Social summary U}
SOcat <- filter(Uclusteranalysis,Cluster == "Social") %>% group_by(`Course Title`) %>% summarise(Cluster=n())
SOcat <- SOcat  %>%
     arrange(desc(Cluster))
SOcat$row_num <- seq.int(nrow(SOcat))
SOcat <- SOcat[,c(3,1,2)]
datatable(SOcat, rownames = FALSE, colnames=c('Udemy Titles for Social Sector', 'Count'))
```


### Top Security

```{r Security summary U}
SEcat <- filter(Uclusteranalysis,Cluster == "Security") %>% group_by(`Course Title`) %>% summarise(Cluster=n())
SEcat <- SEcat  %>%
     arrange(desc(Cluster))
SEcat$row_num <- seq.int(nrow(SEcat))
SEcat <- SEcat[,c(3,1,2)]
datatable(SEcat, rownames = FALSE, colnames=c('Udemy Titles for Security', 'Count'))
```


### Top Economy

```{r Economy summary U}
ECcat <- filter(Uclusteranalysis,Cluster == "Economy Building") %>% group_by(`Course Title`) %>% summarise(Cluster=n())
ECcat <- ECcat  %>%
     arrange(desc(Cluster))
ECcat$row_num <- seq.int(nrow(ECcat))
ECcat <- ECcat[,c(3,1,2)]
datatable(ECcat, rownames = FALSE, colnames=c('Udemy Titles for Economy Building', 'Count'))
```


Time Series
========================================

```{r monthly plot H}
clusteranalysis$Last.Activity.Date <- as.Date(as.character(clusteranalysis$Last.Activity.Date), format="%d-%b-%y")
clusteH <- clusteranalysis[rev(order(as.Date(clusteranalysis$Last.Activity.Date))),]
tsh <- as.data.frame(table(clusteH$Last.Activity.Date))

DE <- c("Date Enrolled")
DE = Uclusteranalysis[DE]
DE <- subset(DE, DE$`Date Enrolled` > (Sys.Date() - 180))
DE$`Date Enrolled` <- format(as.Date(DE$`Date Enrolled`), "%Y-%m-%d")
DE <- DE[rev(order(DE$`Date Enrolled`)),]
tsu <-as.data.frame(table(DE))
colnames(tsu)[1] <- "Var1"
tsboth <- merge(tsh,tsu,by="Var1")
colnames(tsboth)[1] <- "Date"
colnames(tsboth)[2] <- "Accesses"
colnames(tsboth)[3] <- "Accessesu"

plot_ly(tsboth, x = ~Date, y = ~Accesses,  type = 'scatter', mode = 'lines', name = 'Harvard Views', line = list(color = "#145A32")) %>% layout(title = "Harvard & Udemy Accesses Over Time",xaxis= list(autotick = F, tickmode = "array", tickvals = c(6,12,24))) %>% add_trace(y = ~Accessesu, name = 'Udemy Enrolment', mode = 'lines', line = list(color = "#3498DB")) %>% rangeslider()
```


Highlights {style="position:relative;"}
========================================

Column  {.tabset .tabset-fade}
----------------------------------- 

### Harvard Top 10 Agencies

```{r Harvard tree map}
tclusteranalysis <- clusteranalysis[!(clusteranalysis$Agency=="GMAIL"),]
tclusteranalysis <- tclusteranalysis[!(tclusteranalysis$Agency=="YAHOO"),]
tclusteranalysis <- tclusteranalysis[!(tclusteranalysis$Agency=="CSCOLLEGE"),]
 hmap <- tclusteranalysis %>%
     select(Agency) %>%
     unnest(Agency) %>%
     group_by(Agency) %>%
     summarise(Count = n()) %>%
     arrange(desc(Count))%>%head(10)
 hmap %>%
  hchart(type = "treemap", hcaes(x = Agency, value = Count, color = Count))
 
```


### Top 3 Subjects
```{r WOG Harvard pie chart 1}
n <- length(table(Harvard$Subject))
H3<-sort(table(Harvard$Subject),partial=n-3)[n-3]
pie1 <- Harvard %>%
         group_by(Subject) %>%
         summarise(count = n()) %>%
         filter(count>H3) %>%
         plot_ly(labels = ~Subject,
                 values = ~count,
                 textposition = "inside",
                 textinfo = "percent+label",
                 insidetextorientation='horizontal',
                 marker = list(colors = c("#A9CCE3","#A3E4D7","#D7BDE2"))) %>%
           add_pie(hole = 0.5) %>%
         layout(xaxis = list(zeroline = F,
                             showline = F,
                             showticklabels = F,
                             showgrid = F),
                yaxis = list(zeroline = F,
                             showline = F,
                             showticklabels=F,
                             showgrid=F), showlegend=FALSE, annotations=list(text="Top 3 Subjects","showarrow"=F))
pie1
```


### Top Words Harvard Titles
```{r wordcloud}
docs <- Corpus(VectorSource(Hdf$Asset))
toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
docs <- tm_map(docs, toSpace, "/")
docs <- tm_map(docs, toSpace, "@")
docs <- tm_map(docs, toSpace, "\\|")
docs <- tm_map(docs, content_transformer(tolower)) # Convert the text to lower case
docs <- tm_map(docs, removeWords, stopwords("english")) # Remove english common stopwords
docs <- tm_map(docs, removeNumbers) # Remove numbers
docs <- tm_map(docs, removeWords, c("hbr", "actually","ways","can","make","makes")) # Remove custom stop words
docs <- tm_map(docs, removePunctuation) # Remove punctuations
docs <- tm_map(docs, stripWhitespace) # Eliminate extra white spaces
dtm <- TermDocumentMatrix(docs)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
set.seed(1234)
wordcloud(words = d$word, freq = d$freq, min.freq = 5,
          max.words=60, random.order=FALSE, rot.per=0, 
          colors=brewer.pal(4, "BrBG"),family = "serif", font = 2)
```



Column {.tabset .tabset-fade}
----------------------------------- 


### Udemy Top 10 Agencies
```{r Udemy Tree Map}
tuclusteranalysis <- Uclusteranalysis[!(Uclusteranalysis$Agency=="GMAIL"),]
umap <- tuclusteranalysis %>%
         select(Agency) %>%
         unnest(Agency) %>%
         group_by(Agency) %>%
         summarise(Count = n()) %>%
         arrange(desc(Count))%>%head(10)
     
hctreemap2(umap, group_vars = "Agency", size_var = "Count") 
```

### Top 3 Categories

```{r WOG Udemy pie chart 2}
n <- length(table(UDEMY$Subject))
U3<-sort(table(UDEMY$Subject),partial=n-3)[n-3]
pie2 <- UDEMY %>%
         group_by(Subject) %>%
         summarise(count = n()) %>%
         filter(count>U3) %>% 
         plot_ly(labels = ~Subject,
                 values = ~count,
                 textposition = "inside",
                 textinfo = "percent+label",
                 insidetextorientation='horizontal',
                 marker = list(colors = c("#A9CCE3","#A3E4D7","#D7BDE2"))) %>%
         add_pie(hole = 0.5) %>%
         layout(xaxis = list(zeroline = F,
                             showline = F,
                             showticklabels = F,
                             showgrid = F),
                yaxis = list(zeroline = F,
                             showline = F,
                             showticklabels=F,
                             showgrid=F), showlegend=FALSE, annotations=list(text="Top 3 Categories","showarrow"=F))
pie2
```



### Top Words Udemy Titles
```{r wordcloud udemy}
docs <- Corpus(VectorSource(UDEMY$`Course Title`))
toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
docs <- tm_map(docs, toSpace, "/")
docs <- tm_map(docs, toSpace, "@")
docs <- tm_map(docs, toSpace, "\\|")
# Convert the text to lower case
docs <- tm_map(docs, content_transformer(tolower))
# Remove english common stopwords
docs <- tm_map(docs, removeWords, stopwords("english"))
# Remove your own stop word
docs <- tm_map(docs, removeWords, c("cooper","course")) 
# Remove numbers
docs <- tm_map(docs, removeNumbers)
# Remove punctuations
docs <- tm_map(docs, removePunctuation)
# Eliminate extra white spaces
docs <- tm_map(docs, stripWhitespace)
dtm <- TermDocumentMatrix(docs)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
set.seed(1234)
wordcloud(words = d$word, freq = d$freq, min.freq = 5,
          max.words=50, random.order=FALSE, rot.per=0, 
          colors=brewer.pal(4, "BrBG"),family = "serif", font = 2)
```


### Full Categories 

```{r full Udemy barplot}
UDEMY$Subject <- str_extract(UDEMY$`Course Category`, '[[:alnum:]]+')
p3 <- UDEMY %>%
         group_by(Subject) %>%
         summarise(count = n())
p3$Subject <- factor(p3$Subject, levels = unique(p3$Subject)[order(p3$count, decreasing = FALSE)])

         plot_ly(p3, x = ~count,
                 y = ~Subject,
                 color = ~Subject,
                 type = 'bar') %>%
layout(yaxis = list(title = 'Udemy Category'), xaxis = list(title = 'Categories have been grouped by leading word'), showlegend = FALSE)
```


Pivot Table
===========================================

Row  {.tabset .tabset-fade} 
----------------------------------- 

### Harvard PivotTable
```{css, Pivot Table CSS, echo = FALSE}
.rpivotTable{ overflow : scroll; }
```

```{r Pivot Table Harvard}
rpivotTable(clusterH,
            aggregatorName = "Count",
            cols= "Month",
            rows = "Subject",
             rendererName = "Area Chart", menuLimit = 30000)
```


### Udemy PivotTable

```{r Pivot Table Udemy}
rpivotTable(clusterU,
            aggregatorName = "Count",
            cols= "Cluster",
            rows = "Course Category",
            rendererName = "Heatmap", menuLimit = 30000)
```


```{r EDM setup, include=FALSE}
Caps <- function(x) {
  s <- strsplit(x, " ")[[1]]
  paste(toupper(substring(s, 1,1)), substring(s, 2),
        sep="", collapse=" ")
}
trim.trailing <- function (x) sub("-", " ", x)

colnames(clusterU)[2] <- "Asset"
keep <- c("Asset","Cluster") 
clusterh = clusterH[keep]
#create new dataframe of selected columns
clusteru = clusterU[keep]
clusters <- rbind(clusterh, clusteru)
clusters$Asset <- trim.trailing(clusters$Asset)
clusters$Asset <- sapply(clusters$Asset, Caps)
clusters <- as.data.frame(clusters)

n <- length(EDM) 

EDM1 <- EDM[[n]][,1, drop=FALSE]
EDM1 <- tail(EDM1,-4)
EDM1 <- trim.trailing(EDM1$`EDM title`)
EDM1 <- sapply(EDM1, Caps)
EDM1 <- as.data.frame(EDM1)
colnames(EDM1)[1] <- "Asset"
EDM1a <- left_join(EDM1, clusters, by = "Asset")
EDM1a$Count <- rep(1,nrow(EDM1a))
EDM1 <- na.omit(EDM1a)
na1 <- anti_join(EDM1a, EDM1, by="Asset")
EDM1$wrappedx <- sapply(EDM1$Asset, 
                      FUN = function(Asset) {paste(strwrap(Asset, width = 15), collapse = "
")})

EDM2 <- EDM[[n-1]][,1, drop=FALSE]
EDM2 <- tail(EDM2,-4)
EDM2 <- trim.trailing(EDM2$`EDM title`)
EDM2 <- sapply(EDM2, Caps)
EDM2 <- as.data.frame(EDM2)
colnames(EDM2)[1] <- "Asset"
EDM2a <- left_join(EDM2, clusters, by = "Asset")
EDM2a$Count <- rep(1,nrow(EDM2a))
EDM2 <- na.omit(EDM2a)
na2 <- anti_join(EDM2a, EDM2, by="Asset")
EDM2$wrappedx <- sapply(EDM2$Asset, 
                      FUN = function(Asset) {paste(strwrap(Asset, width = 15), collapse = "
")})

EDM3 <- EDM[[n-2]][,1, drop=FALSE]
EDM3 <- tail(EDM3,-4)
EDM3 <- trim.trailing(EDM3$`EDM title`)
EDM3 <- sapply(EDM3, Caps)
EDM3 <- as.data.frame(EDM3)
colnames(EDM3)[1] <- "Asset"
EDM3a <- left_join(EDM3, clusters, by = "Asset")
EDM3a$Count <- rep(1,nrow(EDM3a))
EDM3 <- na.omit(EDM3a)
na3 <- anti_join(EDM3a, EDM3, by="Asset")
EDM3$wrappedx <- sapply(EDM3$Asset, 
                      FUN = function(Asset) {paste(strwrap(Asset, width = 15), collapse = "
")})

EDM4 <- EDM[[n-3]][,1, drop=FALSE]
EDM4 <- tail(EDM4,-4)
EDM4 <- trim.trailing(EDM4$`EDM title`)
EDM4 <- sapply(EDM4, Caps)
EDM4 <- as.data.frame(EDM4)
colnames(EDM4)[1] <- "Asset"
EDM4a <- left_join(EDM4, clusters, by = "Asset")
EDM4a$Count <- rep(1,nrow(EDM4a))
EDM4 <- na.omit(EDM4a)
na4 <- anti_join(EDM4a, EDM4, by="Asset")
EDM4$wrappedx <- sapply(EDM4$Asset, 
                      FUN = function(Asset) {paste(strwrap(Asset, width = 15), collapse = "
")})

EDM5 <- EDM[[n-4]][,1, drop=FALSE]
EDM5 <- tail(EDM5,-4)
EDM5 <- trim.trailing(EDM5$`EDM title`)
EDM5 <- sapply(EDM5, Caps)
EDM5 <- as.data.frame(EDM5)
colnames(EDM5)[1] <- "Asset"
EDM5a <- left_join(EDM5, clusters, by = "Asset")
EDM5a$Count <- rep(1,nrow(EDM5a))
EDM5 <- na.omit(EDM5a)
na5 <- anti_join(EDM5a, EDM5, by="Asset")
EDM5$wrappedx <- sapply(EDM5$Asset, 
                      FUN = function(Asset) {paste(strwrap(Asset, width = 15), collapse = "
")})

EDM6 <- EDM[[n-5]][,1, drop=FALSE]
EDM6 <- tail(EDM6,-4)
EDM6 <- trim.trailing(EDM6$`EDM title`)
EDM6 <- sapply(EDM6, Caps)
EDM6 <- as.data.frame(EDM6)
colnames(EDM6)[1] <- "Asset"
EDM6a <- left_join(EDM6, clusters, by = "Asset")
EDM6a$Count <- rep(1,nrow(EDM6a))
EDM6 <- na.omit(EDM6a)
na6 <- anti_join(EDM6a, EDM6, by="Asset")
EDM6$wrappedx <- sapply(EDM6$Asset, 
                      FUN = function(Asset) {paste(strwrap(Asset, width = 15), collapse = "
")})

EDM7 <- EDM[[n-6]][,1, drop=FALSE]
EDM7 <- tail(EDM7,-4)
EDM7 <- trim.trailing(EDM7$`EDM title`)
EDM7 <- sapply(EDM7, Caps)
EDM7 <- as.data.frame(EDM7)
colnames(EDM7)[1] <- "Asset"
EDM7a <- left_join(EDM7, clusters, by = "Asset")
EDM7a$Count <- rep(1,nrow(EDM7a))
EDM7 <- na.omit(EDM7a)
na7 <- anti_join(EDM7a, EDM7, by="Asset")
EDM7$wrappedx <- sapply(EDM7$Asset, 
                      FUN = function(Asset) {paste(strwrap(Asset, width = 15), collapse = "
")})

EDM8 <- EDM[[n-7]][,1, drop=FALSE]
EDM8 <- tail(EDM8,-4)
EDM8 <- trim.trailing(EDM8$`EDM title`)
EDM8 <- sapply(EDM8, Caps)
EDM8 <- as.data.frame(EDM8)
colnames(EDM8)[1] <- "Asset"
EDM8a <- left_join(EDM8, clusters, by = "Asset")
EDM8a$Count <- rep(1,nrow(EDM8a))
EDM8 <- na.omit(EDM8a)
na8 <- anti_join(EDM8a, EDM8, by="Asset")
EDM8$wrappedx <- sapply(EDM8$Asset, 
                      FUN = function(Asset) {paste(strwrap(Asset, width = 15), collapse = "
")})

EDM9 <- EDM[[n-8]][,1, drop=FALSE]
EDM9 <- tail(EDM9,-4)
EDM9 <- trim.trailing(EDM9$`EDM title`)
EDM9 <- sapply(EDM9, Caps)
EDM9 <- as.data.frame(EDM9)
colnames(EDM9)[1] <- "Asset"
EDM9a <- left_join(EDM9, clusters, by = "Asset")
EDM9a$Count <- rep(1,nrow(EDM9a))
EDM9 <- na.omit(EDM9a)
na9 <- anti_join(EDM9a, EDM9, by="Asset")
EDM9$wrappedx <- sapply(EDM9$Asset, 
                      FUN = function(Asset) {paste(strwrap(Asset, width = 15), collapse = "
")})

EDM10 <- EDM[[n-9]][,1, drop=FALSE]
EDM10 <- tail(EDM10,-4)
EDM10 <- trim.trailing(EDM10$`EDM title`)
EDM10 <- sapply(EDM10, Caps)
EDM10 <- as.data.frame(EDM10)
colnames(EDM10)[1] <- "Asset"
EDM10a <- left_join(EDM10, clusters, by = "Asset")
EDM10a$Count <- rep(1,nrow(EDM10a))
EDM10 <- na.omit(EDM10a)
na10 <- anti_join(EDM10a, EDM10, by="Asset")
EDM10$wrappedx <- sapply(EDM10$Asset, 
                      FUN = function(Asset) {paste(strwrap(Asset, width = 15), collapse = "
")})


EDM11 <- EDM[[n-10]][,1, drop=FALSE]
EDM11 <- tail(EDM11,-4)
EDM11 <- trim.trailing(EDM11$`EDM title`)
EDM11 <- sapply(EDM11, Caps)
EDM11 <- as.data.frame(EDM11)
colnames(EDM11)[1] <- "Asset"
EDM11a <- left_join(EDM11, clusters, by = "Asset")
EDM11a$Count <- rep(1,nrow(EDM11a))
EDM11 <- na.omit(EDM11a)
na11 <- anti_join(EDM11a, EDM11, by="Asset")
EDM11$wrappedx <- sapply(EDM11$Asset, 
                      FUN = function(Asset) {paste(strwrap(Asset, width = 15), collapse = "
")})

EDM12 <- EDM[[n-11]][,1, drop=FALSE]
EDM12 <- tail(EDM12,-4)
EDM12 <- trim.trailing(EDM12$`EDM title`)
EDM12 <- sapply(EDM12, Caps)
EDM12 <- as.data.frame(EDM12)
colnames(EDM12)[1] <- "Asset"
EDM12a <- left_join(EDM12, clusters, by = "Asset")
EDM12a$Count <- rep(1,nrow(EDM12a))
EDM12 <- na.omit(EDM12a)
na12 <- anti_join(EDM12a, EDM12, by="Asset")
EDM12$wrappedx <- sapply(EDM12$Asset, 
                      FUN = function(Asset) {paste(strwrap(Asset, width = 15), collapse = "
")})

EDM13 <- EDM[[n-12]][,1, drop=FALSE]
EDM13 <- tail(EDM13,-4)
EDM13 <- trim.trailing(EDM13$`EDM title`)
EDM13 <- sapply(EDM13, Caps)
EDM13 <- as.data.frame(EDM13)
colnames(EDM13)[1] <- "Asset"
EDM13a <- left_join(EDM13, clusters, by = "Asset")
EDM13a$Count <- rep(1,nrow(EDM13a))
EDM13 <- na.omit(EDM13a)
na13 <- anti_join(EDM13a, EDM13, by="Asset")
EDM13$wrappedx <- sapply(EDM13$Asset, 
                      FUN = function(Asset) {paste(strwrap(Asset, width = 15), collapse = "
")})

EDM14 <- EDM[[n-13]][,1, drop=FALSE]
EDM14 <- tail(EDM14,-4)
EDM14 <- trim.trailing(EDM14$`EDM title`)
EDM14 <- sapply(EDM14, Caps)
EDM14 <- as.data.frame(EDM14)
colnames(EDM14)[1] <- "Asset"
EDM14a <- left_join(EDM14, clusters, by = "Asset")
EDM14a$Count <- rep(1,nrow(EDM14a))
EDM14 <- na.omit(EDM14a)
na14 <- anti_join(EDM14a, EDM14, by="Asset")
EDM14$wrappedx <- sapply(EDM14$Asset, 
                      FUN = function(Asset) {paste(strwrap(Asset, width = 15), collapse = "
")})

EDM15 <- EDM[[n-14]][,1, drop=FALSE]
EDM15 <- tail(EDM15,-4)
EDM15 <- trim.trailing(EDM15$`EDM title`)
EDM15 <- sapply(EDM15, Caps)
EDM15 <- as.data.frame(EDM15)
colnames(EDM15)[1] <- "Asset"
EDM15a <- left_join(EDM15, clusters, by = "Asset")
EDM15a$Count <- rep(1,nrow(EDM15a))
EDM15 <- na.omit(EDM15a)
na15 <- anti_join(EDM15a, EDM15, by="Asset")
EDM15$wrappedx <- sapply(EDM15$Asset, 
                      FUN = function(Asset) {paste(strwrap(Asset, width = 15), collapse = "
")})
```

`r names(EDM[n])`  {data-navmenu="EDM"} 
===========================================

### `r if(length(unique(EDM1$Asset))>7){print("Drag graph to see more")}`
```{r EDM1}
edm1stats <- EDM1a %>% group_by(Asset) %>% tally()
edm1cluster <- EDM1 %>% group_by(Cluster) %>% tally()
edm1cluster <- na.omit(edm1cluster)
plot_ly(EDM1, type = "bar", x = ~wrappedx, y = ~Count, text = ~Cluster, textposition = "auto", hoverinfo = "text", color = ~Cluster, colors = "Spectral") %>% layout(barmode = "stack") %>% layout(title = names(EDM[n]), xaxis = list(title=FALSE, tickangle = 0,  range = c(0,if(length(unique(EDM1$Asset))>7){7})), yaxis = list(title="Count by Cluster"), dragmode = "pan")
```

EDM Statistics {.sidebar data-width=300}
-----------------------------------------------------------------------

There are `r length(unique(EDM1a$Asset))` titles in this EDM

On average, there are `r round(mean(edm1stats$n),2)` views per title.

The most popular title in this EDM and its view count is: `r head(subset(edm1stats, edm1stats[,2] == max(edm1stats$n)),1)` views. 

The highest viewing cluster and its cumulative view count is: `r head(subset(edm1cluster, edm1cluster[,2] == max(edm1cluster$n)),1)` views.

The lowest viewing cluster and its cumulative view count is `r head(subset(edm1cluster, edm1cluster[,2] == min(edm1cluster$n)),1)` views. 

There is/are `r length(unique(na1$Asset))` title(s) without views from any cluster: `r unique(na1$Asset)`


`r names(EDM[n-1])`  {data-navmenu="EDM"}
===========================================
### `r if(length(unique(EDM1$Asset))>7){print("Drag graph to see more")}`
```{r EDM2}
edm2stats <- EDM2a %>% group_by(Asset) %>% tally()
edm2cluster <- EDM2 %>% group_by(Cluster) %>% tally()
edm2cluster <- na.omit(edm2cluster)
plot_ly(EDM2, type = "bar", x = ~wrappedx, y = ~Count, color = ~Cluster, colors = "Spectral", text = ~Cluster, textposition = "auto", hoverinfo = "text") %>% layout(barmode = "stack") %>% layout(title = names(EDM[n-1]),xaxis = list(title=FALSE, tickangle = 0, range = c(0,if(length(unique(EDM2$Asset))>7){7})), yaxis = list(title="Count by Cluster"), dragmode = "pan")
```

EDM Statistics {.sidebar data-width=300}
-----------------------------------------------------------------------

There are `r length(unique(EDM2a$Asset))` titles in this EDM

On average, there are `r round(mean(edm2stats$n),2)` views per title.

The most popular title in this EDM and its view count is: `r head(subset(edm2stats, edm2stats[,2] == max(edm2stats$n)),1)` views. 

The highest viewing cluster and its cumulative view count is: `r head(subset(edm2cluster, edm2cluster[,2] == max(edm2cluster$n)),1)` views.

The lowest viewing cluster and its cumulative view count is `r head(subset(edm2cluster, edm2cluster[,2] == min(edm2cluster$n)),1)` views. 

There is/are `r length(unique(na2$Asset))` title(s) without views from any cluster: `r unique(na2$Asset)`

`r names(EDM[n-2])` {data-navmenu="EDM"}
===========================================
### `r if(length(unique(EDM3$Asset))>7){print("Drag graph to see more")}`
```{r EDM3}
edm3stats <- EDM3a %>% group_by(Asset) %>% tally()
edm3cluster <- EDM3 %>% group_by(Cluster) %>% tally()
edm3cluster <- na.omit(edm3cluster)
plot_ly(EDM3, type = "bar", x = ~wrappedx, y = ~Count, color = ~Cluster, colors = "Spectral", text = ~Cluster, textposition = "auto", hoverinfo = "text") %>% layout(barmode = "stack") %>% layout(title = names(EDM[n-2]),xaxis = list(title=FALSE, tickangle = 0, range = c(0,if(length(unique(EDM3$Asset))>7){7})), yaxis = list(title="Count by Cluster"), dragmode = "pan")
```

EDM Statistics {.sidebar data-width=300}
-----------------------------------------------------------------------

There are `r length(unique(EDM3a$Asset))` titles in this EDM

On average, there are `r round(mean(edm3stats$n),2)` views per title.

The most popular title in this EDM and its view count is: `r head(subset(edm3stats, edm3stats[,2] == max(edm3stats$n)),1)` views. 

The highest viewing cluster and its cumulative view count is: `r head(subset(edm3cluster, edm3cluster[,2] == max(edm3cluster$n)),1)` views.

The lowest viewing cluster and its cumulative view count is `r head(subset(edm3cluster, edm3cluster[,2] == min(edm3cluster$n)),1)` views. 

There is/are `r length(unique(na3$Asset))` title(s) without views from any cluster: `r unique(na3$Asset)`

`r names(EDM[n-3])` {data-navmenu="EDM"}
===========================================
### `r if(length(unique(EDM4$Asset))>7){print("Drag graph to see more")}`
```{r EDM4}
edm4stats <- EDM4a %>% group_by(Asset) %>% tally()
edm4cluster <- EDM4 %>% group_by(Cluster) %>% tally()
edm4cluster <- na.omit(edm4cluster)
plot_ly(EDM4, type = "bar", x = ~wrappedx, y = ~Count, color = ~Cluster, colors = "Spectral", text = ~Cluster, textposition = "auto", hoverinfo = "text") %>% layout(barmode = "stack") %>% layout(title = names(EDM[n-3]),xaxis = list(title=FALSE, tickangle = 0, range = c(0,if(length(unique(EDM4$Asset))>7){7})), yaxis = list(title="Count by Cluster"), dragmode = "pan") 
```

EDM Statistics {.sidebar data-width=300}
-----------------------------------------------------------------------

There are `r length(unique(EDM4a$Asset))` titles in this EDM

On average, there are `r round(mean(edm4stats$n),2)` views per title.

The most popular title in this EDM and its view count is: `r head(subset(edm4stats, edm4stats[,2] == max(edm4stats$n)),1)` views. 

The highest viewing cluster and its cumulative view count is: `r head(subset(edm4cluster, edm4cluster[,2] == max(edm4cluster$n)),1)` views.

The lowest viewing cluster and its cumulative view count is `r head(subset(edm4cluster, edm4cluster[,2] == min(edm4cluster$n)),1)` views. 

There is/are `r length(unique(na4$Asset))` title(s) without views from any cluster: `r unique(na4$Asset)`

`r names(EDM[n-4])` {data-navmenu="EDM"}
===========================================

### `r if(length(unique(EDM5$Asset))>7){print("Drag graph to see more")}`
```{r EDM5}
edm5stats <- EDM5a %>% group_by(Asset) %>% tally()
edm5cluster <- EDM5 %>% group_by(Cluster) %>% tally()
edm5cluster <- na.omit(edm5cluster)
plot_ly(EDM5, type = "bar", x = ~wrappedx, y = ~Count, color = ~Cluster, colors = "Spectral", text = ~Cluster, textposition = "auto", hoverinfo = "text") %>% layout(barmode = "stack") %>% layout(title = names(EDM[n-4]),xaxis = list(title=FALSE, tickangle = 0, range = c(0,if(length(unique(EDM5$Asset))>7){7})), yaxis = list(title="Count by Cluster"), dragmode = "pan")
```

EDM Statistics {.sidebar data-width=300}
-----------------------------------------------------------------------

There are `r length(unique(EDM5a$Asset))` titles in this EDM

On average, there are `r round(mean(edm5stats$n),2)` views per title.

The most popular title in this EDM and its view count is: `r head(subset(edm5stats, edm5stats[,2] == max(edm5stats$n)),1)` views. 

The highest viewing cluster and its cumulative view count is: `r head(subset(edm5cluster, edm5cluster[,2] == max(edm5cluster$n)),1)` views.

The lowest viewing cluster and its cumulative view count is `r head(subset(edm5cluster, edm5cluster[,2] == min(edm5cluster$n)),1)` views. 

There is/are `r length(unique(na5$Asset))` title(s) without views from any cluster: `r unique(na5$Asset)`

`r names(EDM[n-5])` {data-navmenu="EDM"}
===========================================
### `r if(length(unique(EDM6$Asset))>7){print("Drag graph to see more")}`
```{r EDM6}
edm6stats <- EDM6a %>% group_by(Asset) %>% tally()
edm6cluster <- EDM6 %>% group_by(Cluster) %>% tally()
edm6cluster <- na.omit(edm6cluster)
plot_ly(EDM6, type = "bar", x = ~wrappedx, y = ~Count, color = ~Cluster, colors = "Spectral", text = ~Cluster, textposition = "auto", hoverinfo = "text") %>% layout(barmode = "stack") %>% layout(title = names(EDM[n-5]),xaxis = list(title=FALSE, tickangle = 0, range = c(0,if(length(unique(EDM6$Asset))>7){7})), yaxis = list(title="Count by Cluster"), dragmode = "pan")
```

EDM Statistics {.sidebar data-width=300}
-----------------------------------------------------------------------

There are `r length(unique(EDM6a$Asset))` titles in this EDM

On average, there are `r round(mean(edm6stats$n),2)` views per title.

The most popular title in this EDM and its view count is: `r head(subset(edm6stats, edm6stats[,2] == max(edm6stats$n)),1)` views. 

The highest viewing cluster and its cumulative view count is: `r head(subset(edm6cluster, edm6cluster[,2] == max(edm6cluster$n)),1)` views.

The lowest viewing cluster and its cumulative view count is `r head(subset(edm6cluster, edm6cluster[,2] == min(edm6cluster$n)),1)` views. 

There is/are `r length(unique(na6$Asset))` title(s) without views from any cluster: `r unique(na6$Asset)`

`r names(EDM[n-6])` {data-navmenu="EDM"}
===========================================
### `r if(length(unique(EDM7$Asset))>7){print("Drag graph to see more")}`
```{r EDM7}
edm7stats <- EDM7a %>% group_by(Asset) %>% tally()
edm7cluster <- EDM7 %>% group_by(Cluster) %>% tally()
edm7cluster <- na.omit(edm7cluster)
plot_ly(EDM7, type = "bar", x = ~wrappedx, y = ~Count, color = ~Cluster, colors = "Spectral", text = ~Cluster, textposition = "auto", hoverinfo = "text") %>% layout(barmode = "stack") %>% layout(title = names(EDM[n-6]),xaxis = list(title=FALSE, tickangle = 0, range = c(0,if(length(unique(EDM7$Asset))>7){7})), yaxis = list(title="Count by Cluster"), dragmode = "pan")
```

EDM Statistics {.sidebar data-width=300}
-----------------------------------------------------------------------

There are `r length(unique(EDM7a$Asset))` titles in this EDM

On average, there are `r round(mean(edm7stats$n),2)` views per title.

The most popular title in this EDM and its view count is: `r head(subset(edm7stats, edm7stats[,2] == max(edm7stats$n)),1)` views. 

The highest viewing cluster and its cumulative view count is: `r head(subset(edm7cluster, edm7cluster[,2] == max(edm7cluster$n)),1)` views.

The lowest viewing cluster and its cumulative view count is `r head(subset(edm7cluster, edm7cluster[,2] == min(edm7cluster$n)),1)` views. 

There is/are `r length(unique(na7$Asset))` title(s) without views from any cluster: `r unique(na7$Asset)`

`r names(EDM[n-7])` {data-navmenu="EDM"}
===========================================
### `r if(length(unique(EDM8$Asset))>7){print("Drag graph to see more")}`
```{r EDM8}
edm8stats <- EDM8a %>% group_by(Asset) %>% tally()
edm8cluster <- EDM8 %>% group_by(Cluster) %>% tally()
edm8cluster <- na.omit(edm8cluster)
plot_ly(EDM8, type = "bar", x = ~wrappedx, y = ~Count, color = ~Cluster, colors = "Spectral", text = ~Cluster, textposition = "auto", hoverinfo = "text") %>% layout(barmode = "stack") %>% layout(title = names(EDM[n-7]),xaxis = list(title=FALSE, tickangle = 0, range = c(0,if(length(unique(EDM8$Asset))>7){7})), yaxis = list(title="Count by Cluster"), dragmode = "pan")
```

EDM Statistics {.sidebar data-width=300}
-----------------------------------------------------------------------

There are `r length(unique(EDM8a$Asset))` titles in this EDM

On average, there are `r round(mean(edm8stats$n),2)` views per title.

The most popular title in this EDM and its view count is: `r head(subset(edm8stats, edm8stats[,2] == max(edm8stats$n)),1)` views. 

The highest viewing cluster and its cumulative view count is: `r head(subset(edm8cluster, edm8cluster[,2] == max(edm8cluster$n)),1)` views.

The lowest viewing cluster and its cumulative view count is `r head(subset(edm8cluster, edm8cluster[,2] == min(edm8cluster$n)),1)` views. 

There is/are `r length(unique(na8$Asset))` title(s) without views from any cluster: `r unique(na8$Asset)`

`r names(EDM[n-8])` {data-navmenu="EDM"}
===========================================
### `r if(length(unique(EDM9$Asset))>7){print("Drag graph to see more")}`
```{r EDM9}
edm9stats <- EDM9a %>% group_by(Asset) %>% tally()
edm9cluster <- EDM9 %>% group_by(Cluster) %>% tally()
edm9cluster <- na.omit(edm9cluster)
plot_ly(EDM9, type = "bar", x = ~wrappedx, y = ~Count, color = ~Cluster, colors = "Spectral", text = ~Cluster, textposition = "auto", hoverinfo = "text") %>% layout(barmode = "stack") %>% layout(title = names(EDM[n-8]),xaxis = list(title=FALSE, tickangle = 0, range = c(0,if(length(unique(EDM9$Asset))>7){7})), yaxis = list(title="Count by Cluster"), dragmode = "pan")
```

EDM Statistics {.sidebar data-width=300}
-----------------------------------------------------------------------

There are `r length(unique(EDM9a$Asset))` titles in this EDM

On average, there are `r round(mean(edm9stats$n),2)` views per title.

The most popular title in this EDM and its view count is: `r head(subset(edm9stats, edm9stats[,2] == max(edm9stats$n)),1)` views. 

The highest viewing cluster and its cumulative view count is: `r head(subset(edm9cluster, edm9cluster[,2] == max(edm9cluster$n)),1)` views.

The lowest viewing cluster and its cumulative view count is `r head(subset(edm9cluster, edm9cluster[,2] == min(edm9cluster$n)),1)` views. 

There is/are `r length(unique(na9$Asset))` title(s) without views from any cluster: `r unique(na9$Asset)`

`r names(EDM[n-9])` {data-navmenu="EDM"}
===========================================
### `r if(length(unique(EDM10$Asset))>7){print("Drag graph to see more")}`
```{r EDM10}
edm10stats <- EDM10a %>% group_by(Asset) %>% tally()
edm10cluster <- EDM10 %>% group_by(Cluster) %>% tally()
edm10cluster <- na.omit(edm10cluster)
plot_ly(EDM10, type = "bar", x = ~wrappedx, y = ~Count, color = ~Cluster, colors = "Spectral", text = ~Cluster, textposition = "auto", hoverinfo = "text") %>% layout(barmode = "stack") %>% layout(title = names(EDM[n-9]),xaxis = list(title=FALSE, tickangle = 0, range = c(0,if(length(unique(EDM10$Asset))>7){7})), yaxis = list(title="Count by Cluster"), dragmode = "pan")
```

EDM Statistics {.sidebar data-width=300}
-----------------------------------------------------------------------

There are `r length(unique(EDM10a$Asset))` titles in this EDM

On average, there are `r round(mean(edm10stats$n),2)` views per title.

The most popular title in this EDM and its view count is: `r head(subset(edm10stats, edm10stats[,2] == max(edm10stats$n)),1)` views. 

The highest viewing cluster and its cumulative view count is: `r head(subset(edm10cluster, edm10cluster[,2] == max(edm10cluster$n)),1)` views.

The lowest viewing cluster and its cumulative view count is `r head(subset(edm10cluster, edm10cluster[,2] == min(edm10cluster$n)),1)` views. 

There is/are `r length(unique(na10$Asset))` title(s) without views from any cluster: `r unique(na10$Asset)`

`r names(EDM[n-10])` {data-navmenu="EDM"}
===========================================
### `r if(length(unique(EDM11$Asset))>7){print("Drag graph to see more")}`
```{r EDM11}
edm11stats <- EDM11a %>% group_by(Asset) %>% tally()
edm11cluster <- EDM11 %>% group_by(Cluster) %>% tally()
edm11cluster <- na.omit(edm11cluster)
plot_ly(EDM11, type = "bar", x = ~wrappedx, y = ~Count, color = ~Cluster, colors = "Spectral", text = ~Cluster, textposition = "auto", hoverinfo = "text") %>% layout(barmode = "stack") %>% layout(title = names(EDM[n-10]),xaxis = list(title=FALSE, tickangle = 0, range = c(0,if(length(unique(EDM11$Asset))>7){7})), yaxis = list(title="Count by Cluster"), dragmode = "pan")
```

EDM Statistics {.sidebar data-width=300}
-----------------------------------------------------------------------

There are `r length(unique(EDM11a$Asset))` titles in this EDM

On average, there are `r round(mean(edm11stats$n),2)` views per title.

The most popular title in this EDM and its view count is: `r head(subset(edm11stats, edm11stats[,2] == max(edm11stats$n)),1)` views. 

The highest viewing cluster and its cumulative view count is: `r head(subset(edm11cluster, edm11cluster[,2] == max(edm11cluster$n)),1)` views.

The lowest viewing cluster and its cumulative view count is `r head(subset(edm11cluster, edm11cluster[,2] == min(edm11cluster$n)),1)` views. 

There is/are `r length(unique(na11$Asset))` title(s) without views from any cluster: `r unique(na11$Asset)`

`r names(EDM[n-11])` {data-navmenu="EDM"}
===========================================
### `r if(length(unique(EDM12$Asset))>7){print("Drag graph to see more")}`
```{r EDM12}
edm12stats <- EDM12a %>% group_by(Asset) %>% tally()
edm12cluster <- EDM12 %>% group_by(Cluster) %>% tally()
edm12cluster <- na.omit(edm12cluster)
plot_ly(EDM12, type = "bar", x = ~wrappedx, y = ~Count, color = ~Cluster, colors = "Spectral", text = ~Cluster, textposition = "auto", hoverinfo = "text") %>% layout(barmode = "stack") %>% layout(title = names(EDM[n-11]),xaxis = list(title=FALSE, tickangle = 0, range = c(0,if(length(unique(EDM12$Asset))>7){7})), yaxis = list(title="Count by Cluster"), dragmode = "pan")
```

EDM Statistics {.sidebar data-width=300}
-----------------------------------------------------------------------

There are `r length(unique(EDM12a$Asset))` titles in this EDM

On average, there are `r round(mean(edm12stats$n),2)` views per title.

The most popular title in this EDM and its view count is: `r head(subset(edm12stats, edm12stats[,2] == max(edm12stats$n)),1)` views. 

The highest viewing cluster and its cumulative view count is: `r head(subset(edm12cluster, edm12cluster[,2] == max(edm12cluster$n)),1)` views.

The lowest viewing cluster and its cumulative view count is `r head(subset(edm12cluster, edm12cluster[,2] == min(edm12cluster$n)),1)` views. 

There is/are `r length(unique(na12$Asset))` title(s) without views from any cluster: `r unique(na12$Asset)`

`r names(EDM[n-12])` {data-navmenu="EDM"}
===========================================
### `r if(length(unique(EDM13$Asset))>7){print("Drag graph to see more")}`
```{r EDM13}
edm13stats <- EDM13 %>% group_by(Asset) %>% tally()
edm13cluster <- EDM13 %>% group_by(Cluster) %>% tally()
edm13cluster <- na.omit(edm13cluster)
plot_ly(EDM13, type = "bar", x = ~wrappedx, y = ~Count, color = ~Cluster, colors = "Spectral", text = ~Cluster, textposition = "auto", hoverinfo = "text") %>% layout(barmode = "stack") %>% layout(title = names(EDM[n-12]),xaxis = list(title=FALSE, tickangle = 0, range = c(0,if(length(unique(EDM13$Asset))>7){7})), yaxis = list(title="Count by Cluster"), dragmode = "pan")
```

EDM Statistics {.sidebar data-width=300}
-----------------------------------------------------------------------

There are `r length(unique(EDM13a$Asset))` titles in this EDM

On average, there are `r round(mean(edm13stats$n),2)` views per title.

The most popular title in this EDM and its view count is: `r head(subset(edm13stats, edm13stats[,2] == max(edm13stats$n)),1)` views. 

The highest viewing cluster and its cumulative view count is: `r head(subset(edm13cluster, edm13cluster[,2] == max(edm13cluster$n)),1)` views.

The lowest viewing cluster and its cumulative view count is `r head(subset(edm13cluster, edm13cluster[,2] == min(edm13cluster$n)),1)` views. 

There is/are `r length(unique(na13$Asset))` title(s) without views from any cluster: `r unique(na13$Asset)`

`r names(EDM[n-13])` {data-navmenu="EDM"}
===========================================
### `r if(length(unique(EDM14$Asset))>7){print("Drag graph to see more")}`
```{r EDM14}
edm14stats <- EDM14 %>% group_by(Asset) %>% tally()
edm14cluster <- EDM14 %>% group_by(Cluster) %>% tally()
edm14cluster <- na.omit(edm14cluster)
plot_ly(EDM14, type = "bar", x = ~wrappedx, y = ~Count, color = ~Cluster, colors = "Spectral", text = ~Cluster, textposition = "auto", hoverinfo = "text") %>% layout(barmode = "stack") %>% layout(title = names(EDM[n-13]),xaxis = list(title=FALSE, tickangle = 0, range = c(0,if(length(unique(EDM14$Asset))>7){7})), yaxis = list(title="Count by Cluster"), dragmode = "pan")
```

EDM Statistics {.sidebar data-width=300}
-----------------------------------------------------------------------

There are `r length(unique(EDM14a$Asset))` titles in this EDM

On average, there are `r round(mean(edm14stats$n),2)` views per title.

The most popular title in this EDM and its view count is: `r head(subset(edm14stats, edm14stats[,2] == max(edm14stats$n)),1)` views. 

The highest viewing cluster and its cumulative view count is: `r head(subset(edm14cluster, edm14cluster[,2] == max(edm14cluster$n)),1)` views.

The lowest viewing cluster and its cumulative view count is `r head(subset(edm14cluster, edm14cluster[,2] == min(edm14cluster$n)),1)` views. 

There is/are `r length(unique(na14$Asset))` title(s) without views from any cluster: `r unique(na14$Asset)`

`r names(EDM[n-14])` {data-navmenu="EDM"}
===========================================
### `r if(length(unique(EDM15$Asset))>7){print("Drag graph to see more")}`
```{r EDM15}
edm15stats <- EDM15 %>% group_by(Asset) %>% tally()
edm15cluster <- EDM15 %>% group_by(Cluster) %>% tally()
edm15cluster <- na.omit(edm15cluster)
plot_ly(EDM15, type = "bar", x = ~wrappedx, y = ~Count, color = ~Cluster, colors = "Spectral", text = ~Cluster, textposition = "auto", hoverinfo = "text") %>% layout(barmode = "stack") %>% layout(title = names(EDM[n-14]),xaxis = list(title=FALSE, tickangle = 0, range = c(0,if(length(unique(EDM15$Asset))>7){7})), yaxis = list(title="Count by Cluster"), dragmode = "pan")
```

EDM Statistics {.sidebar data-width=400}
-----------------------------------------------------------------------

There are `r length(unique(EDM15a$Asset))` titles in this EDM

On average, there are `r round(mean(edm15stats$n),2)` views per title.

The most popular title in this EDM and its view count is: `r head(subset(edm15stats, edm15stats[,2] == max(edm15stats$n)),1)` views. 

The highest viewing cluster and its cumulative view count is: `r head(subset(edm15cluster, edm15cluster[,2] == max(edm15cluster$n)),1)` views.

The lowest viewing cluster and its cumulative view count is `r head(subset(edm15cluster, edm15cluster[,2] == min(edm15cluster$n)),1)` views. 

There is/are `r length(unique(na15$Asset))` title(s) without views from any cluster: `r unique(na15$Asset)`

Summary  {data-orientation=columns}
===========================================

Column {data-width=350}
-----------------------------------

### Total Harvard views **Security** 

```{r Social}
So <- filter(clusteranalysis,Cluster == "Security") %>% summarise(Cluster=n())
valueBox(So, icon = "fa-shield-alt", color = "#1ABC9C")
```


### Total Harvard views **Social** 

```{r Infrastructure}
In <- filter(clusteranalysis,Cluster == "Social") %>% summarise(Cluster=n())
valueBox(In, icon = "fa-user", color = "#48C9B0")
```


### Total Harvard views **Infrastructure**

```{r Central Admin Sector}
CA <- filter(clusteranalysis,Cluster == "Infrastructure and Environment") %>% summarise(Cluster=n())
valueBox(CA, icon = "fa-building", color = "#76D7C4")
```


### Total Harvard views **Central Admin** 

```{r Security}
Sec <- filter(clusteranalysis,Cluster == "Central Administration") %>% summarise(Cluster=n())
valueBox(Sec, icon = "fa-home", color = "#A3E4D7")  
```

### Total Harvard views **Economy Building** 

```{r Economy}
Eco <- filter(clusteranalysis,Cluster == "Economy Building") %>% summarise(Cluster=n())
valueBox(Eco, icon = "fa-coins", color = "#D1F2EB")
totalH$n <- as.numeric(totalH$n)
```

### % of Harvard views from **TCs** 

```{r others}
other <- filter(clusteranalysis,Cluster == "TCs") %>% summarise(Cluster=n())
otherspercent <-sum(other)/sum(totalH$n)*100
valueBox(round(otherspercent), icon = "fa-percent", color = "#E8F8F5")
otherp <- round(otherspercent,1)
```


Column {data-width=350}
-----------------------------------

### Total Udemy accesses **Security** 

```{r Security U}
SecU <- filter(Uclusteranalysis,Cluster == "Security") %>% summarise(Cluster=n())
valueBox(SecU, icon = "fa-shield-alt", color = "#2980B9")
```


### Total Udemy accesses **Social** 

```{r Social U}
SoU <- filter(Uclusteranalysis,Cluster == "Social") %>% summarise(Cluster=n())
valueBox(SoU, icon = "fa-user", color = "#5499C7")
```


### Total Udemy accesses **Infrastructure** 

```{r Infrastructure U}
InU <- filter(Uclusteranalysis,Cluster == "Infrastructure and Environment") %>% summarise(Cluster=n())
valueBox(InU, icon = "fa-building", color = "#7FB3D5") 
```


### Total Udemy accesses **Central Admin**

```{r Central Admin Sector U}
CAU <- filter(Uclusteranalysis,Cluster == "Central Administration") %>% summarise(Cluster=n())
valueBox(CAU, icon = "fa-home", color = "#A9CCE3")
```


### Total Udemy accesses **Economy Building** 

```{r Economy U}
EcoU <- filter(Uclusteranalysis,Cluster == "Economy Building") %>% summarise(Cluster=n())
valueBox(EcoU, icon = "fa-coins", color = "#D4E6F1")
totalU$n <- as.numeric(totalU$n) 
```

### % of Udemy accesses from **TCs** 

```{r others U}
otherU <- filter(Uclusteranalysis,Cluster == "TCs") %>% summarise(Cluster=n())
otherspercentU <-sum(otherU)/sum(totalU$n)*100
valueBox(round(otherspercentU), icon = "fa-percent", color = "#EAF2F8")
Uotherp <- round(otherspercentU,1)
```


```{r Harvard titles 2, include=FALSE}
totalH <- clusteranalysis %>% group_by(Asset) %>% tally()
datatable(totalH, rownames = FALSE, colnames=c('Harvard Titles', 'Count'))
totalU <- Uclusteranalysis %>% group_by(`Course Title`) %>% tally()
datatable(totalH, rownames = FALSE, colnames=c('Udemy Titles', 'Count'))
```

```{r include=FALSE}
totalH$n <- as.numeric(totalH$n)
title <- subset(totalH, totalH[,2] == max(totalH$n))

totalU$n <- as.numeric(totalU$n)
titleU <- subset(totalU, totalU[,2] == max(totalU$n))

tot <- as.numeric(other+otherU)
tot <- format(tot,scientific=FALSE)
```

Findings and Patterns {.sidebar data-width=600}
-----------------------------------------------------------------------

>Findings and Patterns

Training coordinators make up `r tot` counts, which comprises `r otherp`% of Harvard views and `r Uotherp`% of Udemy accesses.

The Social sector has the highest number of both Harvard views and Udemy accesses. Vice versa for the Economy Building sector.

Harvard has `r round((mean(tabl$Count > 1)*100), digits = 2)`% repeated learners (>1 view) and Udemy has `r round((mean(tablU$Count > 1)*100), digits = 2)`% repeated learners (>1 course attendance).

The most popular Harvard subjects are Leadership & Managing People, Organisational Development and Communication. These are consistently the top 3 subjects across all clusters. Leadership & Managing People always has at least 2 times more views than the second highest subject. The least popular subjects include Management, Finance & Accounting and Sales & Marketing.

Udemy course categories that are popular across all clusters include Strategy, Communications, Personal Growth and Analytics. The least popular categories include Compliance, Ops Systems, Database and Finance Fundamenatals. An example of a cluster-unique category is Stress Management, which is only popular for the Security cluster. Also, the Social sector contributes the greatest to the popularity of the Personal Growth and Communication categories. 

The most popular Harvard article and its view count is: `r title ` views. 

The most popular Udemy course and its access count is: `r titleU` accesses. 

* Future insights can be manually edited here on the R script. All figures, dates & numbers (including this box) will be automatically regenerated with new excel data.

Data from this report was collected between `r format(Sys.Date() - 200, format = "%B %d, %Y")` and `r format(Sys.Date(), format = "%B %d, %Y")`
    
Created by: Civil Service College - CCE on `r format(Sys.Date(), format = "%B %d, %Y")`